https://www.ijbme.org/ Iranian Journal of Biomedical Engineering (IJBME) en daily 1 Fri, 22 May 2026 00:00:00 +0330 Fri, 22 May 2026 00:00:00 +0330 https://www.ijbme.org/article_738222.html Human Random Number Generation (RNG) engages several components of executive functions, including working memory, response inhibition, and cognitive flexibility. Human-generated numerical sequences can be conceptualized as bio cognitive signals with chaotic properties. The complexity reduction hypothesis suggests that aging is associated with a decline in the dynamical complexity of cognitive systems. The present study aimed to quantify these dynamical changes in healthy young and older adults using nonlinear dynamical analyses.Thirty healthy participants, including 15 young adults and 15 older adults, were recruited. Each participant performed a Random Number Generation task producing a sequence of 200 numbers (1–9). The resulting time series were analyzed using phase space reconstruction. Measures from Recurrence Quantification Analysis (RQA) and fractal analysis were extracted. Group classification was performed using a Support Vector Machine (SVM).Compared with the young group, the older group showed significantly higher values of Determinism (DET), Mean Diagonal Line Length (L), and Laminarity (LAM), indicating increased repetitive patterns and reduced behavioral variability. The correlation dimension and Lyapunov exponent were significantly lower in the older group, suggesting reduced dynamical complexity and lower sensitivity to initial conditions. The Hurst exponent was higher in the older group, reflecting stronger long range correlations and decreased randomness. The classification accuracy between the two groups reached 91.3%.These findings provide quantitative evidence supporting the complexity reduction hypothesis in cognitive aging. Given its simplicity and low cost, this approach may offer a promising basis for developing cognitive screening tools for older adults. https://www.ijbme.org/article_737782.html Rapid eye movement (REM) sleep is associated with dreaming and emotional processing, yet how dream emotional content is reflected in EEG spectral patterns remains unclear. In this study, EEG spectral features during REM sleep were analyzed to examine whether negative, neutral, and positive dream emotions can be differentiated based on electrophysiological activity. To align EEG recordings with dream reports, the final three minutes of each REM segment before awakening were extracted from DEED. Spectral features were computed from six electrodes: F3, F4, FT7, FT8, T7, and T8. For each electrode, absolute power, relative power, and inter-band power ratios were calculated across delta, theta, alpha, beta, and gamma frequency bands, resulting in 120 EEG features per sample. Differences among the three dream-emotion groups were assessed using one-way analysis of covariance, with REM cycle number as a covariate. Bonferroni and false discovery rate corrections were applied for multiple comparisons, and pairwise comparisons were performed using Welch’s t-test on REM-adjusted values. After Bonferroni correction, 11 EEG spectral features remained significant. These features were located over temporal, fronto-temporal, and frontal electrodes and related to alpha-band indices and delta/alpha ratios. Post-hoc analyses identified 15 significant pairwise differences after FDR correction, with most prominent differences between positive and neutral dreams. Compared with neutral dreams, positive dreams showed lower alpha-related indices and alpha-centered ratios over temporal regions, together with higher frontal delta/alpha ratios. In contrast, negative dreams differed from neutral dreams only in relative delta power at FT7. These findings suggest that REM dream emotion is associated with measurable but subtle EEG spectral changes. The dominant pattern involved reduced temporal alpha-related activity and increased frontal delta/alpha ratios in positive dreams relative to neutral dreams.